
Beware of the Simulated DAG! Varsortability in Additive Noise Models
Additive noise models are a class of causal models in which each variabl...
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Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small pvalues
In this article, we describe the algorithms for causal structure learnin...
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Distributional robustness as a guiding principle for causality in cognitive neuroscience
While probabilistic models describe the dependence structure between obs...
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groupICA: Independent component analysis for grouped data
We introduce groupICA, a novel independent component analysis (ICA) algo...
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Causal Consistency of Structural Equation Models
Complex systems can be modelled at various levels of detail. Ideally, ca...
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A note on the expected minimum error probability in equientropic channels
While the channel capacity reflects a theoretical upper bound on the ach...
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Recovery of nonlinear causeeffect relationships from linearly mixed neuroimaging data
Causal inference concerns the identification of causeeffect relationshi...
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Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation
Optimization on manifolds is a class of methods for optimization of an o...
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Causal and anticausal learning in pattern recognition for neuroimaging
Pattern recognition in neuroimaging distinguishes between two types of m...
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Decoding index finger position from EEG using random forests
While invasively recorded brain activity is known to provide detailed in...
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MERLiN: Mixture Effect Recovery in Linear Networks
Causal inference concerns the identification of causeeffect relationshi...
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Causal interpretation rules for encoding and decoding models in neuroimaging
Causal terminology is often introduced in the interpretation of encoding...
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Sebastian Weichwald
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